Electrostatic Charge Polarity Effect on Respiratory Deposition in the Glass Bead Tracheobronchial Airways Model

The effects of unipolar and bipolar electrostatic charges on the deposition efficiency of therapeutic aerosols in the physical model of human tracheobronchial (TB) airways have been investigated. Respirable size aerosol particles were generated by a commonly prescribed and commercially available nebulizer and charged by a corona charger and then their size and charge distributions were characterized by an Electronic Single ParticleAerodynamic Relaxation Time analyzer to study the drug aerosol particles\u27 deposition pattern. The experiments were performed with a glass bead tracheobronchial model (GBTBM) (physical model) which was designed and developed based upon widely used and adopted dichotomous lung morphometric data presented in the Ewald R. Weibel model. The model was validated with the respiratory deposition data predicted by the International Commission on Radiological Protection and the United States Pharmacopeia (USP) approved Andersen Cascade Impactor (ACI). Unipolarly and bipolarly charged particles were characterized for two configurations: a) without TB model in place and b) with TB model in place. Findings showed that the deposition of unipolarly charged particles was about 3 times of the bipolarly charged particles. It was also found that bioengineered therapeutic aerosols with good combinations ofaerodynamic size and electrostatic charge are good candidates for the administration of respiratory medicinal drugs

Electrostatic Charge Polarity Effect on Respiratory Deposition in the Glass Bead Tracheobronchial Airways Model

The effects of unipolar and bipolar electrostatic charges on the deposition efficiency of therapeutic aerosols in the physical model of human tracheobronchial (TB) airways have been investigated. Respirable size aerosol particles were generated by a commonly prescribed and commercially available nebulizer and charged by a corona charger and then their size and charge distributions were characterized by an Electronic Single Particle Aerodynamic Relaxation Time analyzer to study the drug aerosol particles\u27 deposition pattern. The experiments were performed with a glass bead tracheobronchial model (GBTBM) (physical model) which was designed and developed based upon widely used and adopted dichotomous lung morphometric data presented in the Ewald R. Weibel model. The model was validated with the respiratory deposition data predicted by the International Commission on Radiological Protection and the United States Pharmacopeia (USP) approved Andersen Cascade Impactor (ACI). Unipolarly and bipolarly charged particles were characterized for two configurations: a) without TB model in place and b) with TB model in place. Findings showed that the deposition of unipolarly charged particles was about 3 times of the bipolarly charged particles. It was also found that bioengineered therapeutic aerosols with good combinations of aerodynamic size and electrostatic charge are good candidates for the administration of respiratory medicinal drugs

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

BACKGROUND: Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." RESULTS: The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. CONCLUSION: We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself